Simulation on slope uncertainty derived from DEMs at different resolution levels： a case study in the Loess Plateau

• Published in: Journal of geographical sciences 2003-10, Vol.13 (4), p.387-394
• Main Authors: TANG, G, ZHAO, M, LI, T, LIU, Y, ZHANG, T
• Format: Article
• Language: English
• Subjects: Asia; Bgi / Prodig; China; Data processing; DEM; Digitizing; Land use planning; Physical geography; Simulation; Soil erosion; Spatial analysis; Topographic mapping; 分辨率; 土地利用; 土壤侵蚀; 地图设计; 地形复杂性; 空间分析; 黄土高原
• ISSN: 1009-637X; 1861-9568
• DOI: 10.1007/BF02837875

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as(0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.